71 lines
2.1 KiB
Python
Executable File
71 lines
2.1 KiB
Python
Executable File
import numpy as np
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import cv2
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from rknn.api import RKNN
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if __name__ == '__main__':
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# Create RKNN object
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rknn = RKNN(verbose=True)
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# Pre-process config
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print('--> Config model')
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rknn.config(mean_values=[[127.5, 127.5, 127.5], [0, 0, 0], [0, 0, 0], [127.5]],
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std_values=[[128, 128, 128], [1, 1, 1], [1, 1, 1], [128]])
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print('done')
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# Load model
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print('--> Loading model')
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ret = rknn.load_tensorflow(tf_pb='./conv_128.pb',
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inputs=['input1', 'input2', 'input3', 'input4'],
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outputs=['output'],
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input_size_list=[[1, 128, 128, 3], [1, 128, 128, 3], [1, 128, 128, 3], [1, 128, 128, 1]])
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if ret != 0:
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print('Load model failed!')
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exit(ret)
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print('done')
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# Build model
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print('--> Building model')
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ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
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if ret != 0:
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print('Build model failed!')
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exit(ret)
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print('done')
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# Export rknn model
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print('--> Export rknn model')
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ret = rknn.export_rknn('./conv_128.rknn')
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if ret != 0:
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print('Export rknn model failed!')
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exit(ret)
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print('done')
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# Init runtime environment
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print('--> Init runtime environment')
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ret = rknn.init_runtime()
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if ret != 0:
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print('Init runtime environment failed!')
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exit(ret)
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print('done')
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# Set inputs
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img = cv2.imread('./dog_128x128.jpg')
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # nhwc
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img_gray = cv2.imread('./dog_128x128_gray.png', cv2.IMREAD_GRAYSCALE)
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img_gray = np.expand_dims(img_gray, -1) # nhwc
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input2 = np.load('input2.npy').astype('float32') # nchw
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input3 = np.load('input3.npy').astype('float32') # nchw
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# Inference
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print('--> Running model')
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outputs = rknn.inference(inputs=[img, input2, input3, img_gray], data_format=['nhwc', 'nchw', 'nchw', 'nhwc'])
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np.save('./functions_multi_input_test_0.npy', outputs[0])
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print('done')
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outputs[0] = outputs[0].reshape((1, -1))
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print('inference result: ', outputs)
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rknn.release()
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